Reimagining Bilingual Education: A Linguistically Expansive Orientation
Bibliographic record
Abstract
This article explores the possibilities of a linguistically expansive orientation to two-way immersion (TWI), a bilingual model that has traditionally adopted a “double monolingual” approach to bilingual learning/ers. To illustrate an expansive perspective, we present two case studies undertaken at the same bilingual school that explored strategies for fostering more flexible understandings of students’ linguistic repertoires. Findings from case study one reveal that student identities are more dynamic and contextually-oriented than typically understood within TWI, while also demonstrating that student sense-making can reflect the program’s narrow lens on bilingualism. Findings from case study two illustrate how, when teachers engage students in multilingual writing, meaningful contexts are created for encountering diversity and for expanding and evolving their linguistic repertoires. We argue that a linguistically expansive orientation invites teachers to move beyond language separation in TWI and, in doing so, make space for and affirm students’ dynamic languaging and dynamically lingual identities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".